library(ggplot2)
library("cowplot")
library(ggpubr)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: ‘ggpubr’

The following object is masked from ‘package:cowplot’:

    get_legend
library(phyloseq)
library (ggsci)

rr my16stheme <- theme_bw()+ theme(plot.title = element_text(family =, face = , size = (25)), panel.background = element_blank(), panel.border = element_rect(fill=NA,color = , size = 1), panel.spacing= unit(0.7,), strip.text= element_text(size=20, family =, face=), legend.title = element_text(colour = , face = ,family =,size = (20)), legend.text = element_text(face = , colour=,family = ,size = (18) ), axis.title = element_text(family = , face = , size = (20), colour = ), axis.text = element_text(family = ,face = , colour = , size = (18)))

#Data import
phy <- readRDS("breathe_sputum_phyloseq_trial_13Dec2021.RDS")
Trial <-data.frame (sample_data(phy))

#----------__Subsettting data-----------------
colnames(Trial)
  [1] "templateDNA_16SqPCR_copies" "Shannon"                    "Simpson"                   
  [4] "InvSimpson"                 "site"                       "studyno"                   
  [7] "acut_exac12m"               "hospitalised12m"            "reason_hospi"              
 [10] "antibiotic12m"              "any3_reasons"               "any3_reasons.1"            
 [13] "adherence"                  "visit"                      "visit_date"                
 [16] "fev1z"                      "weight"                     "height"                    
 [19] "weightcat"                  "f24mrcscore"                "f41shuty"                  
 [22] "FEV1_baseline"              "FEVpcpred"                  "FVC"                       
 [25] "FVCZ"                       "FVCpcpred"                  "FEV1FVC"                   
 [28] "FEV1FVCZ"                   "cd4"                        "vload"                     
 [31] "sex"                        "dob"                        "bmi"                       
 [34] "agey"                       "zwauk"                      "zhauk"                     
 [37] "zbmiuk"                     "vllog"                      "vlsup"                     
 [40] "vlsup400"                   "vlsup40"                    "normalbmi"                 
 [43] "normalwaz"                  "normalhaz"                  "durartgp"                  
 [46] "cd4gp"                      "agegpsym"                   "abnrr"                     
 [49] "abnsat"                     "abnhr"                      "startfollowup"             
 [52] "enrolmonth"                 "agegp"                      "ageyart"                   
 [55] "duryart"                    "duryartgp"                  "attendschool"              
 [58] "datehiv"                    "artdrug1stline"             "artdrug2ndline"            
 [61] "endfollowup"                "exitreason"                 "dow"                       
 [64] "dod"                        "lastvisit"                  "visittotno"                
 [67] "lastvisitdate"              "endtreatddate"              "endtreatdate"              
 [70] "treatstopreason"            "group"                      "f25cough"                  
 [73] "f11cotri"                   "f13drugs_3tc"               "f13drugs_abc"              
 [76] "f13drugs_atv"               "f13drugs_azt"               "f13drugs_d4t"              
 [79] "f13drugs_ddi"               "f13drugs_efv"               "f13drugs_lpv"              
 [82] "f13drugs_nvp"               "f13drugs_tnf"               "f13drugs_other"            
 [85] "f13drugs_spec"              "f14adm"                     "f15nadm"                   
 [88] "f16tbtreat"                 "f17notbtreat"               "f18breathe"                
 [91] "f27sputum"                  "sc04sccurrent"              "sc05gradesc"               
 [94] "sc06misssc"                 "sc08repsc"                  "artreg"                    
 [97] "sp03date"                   "sampling_month"             "colmonthgp_mal2"           
[100] "colmonthgp_mal3"            "colmonthgp_zim3"            "colmonthgp1"               
[103] "colmonthgp2"                "sp05time"                   "sp06spvol"                 
[106] "sp07indid"                  "sp08ind"                    "sp09nosamp"                
[109] "sp10osat"                   "sp11eff_bleed"              "sp11eff_vomit"             
[112] "sp11eff_wheeze"             "sp11eff_breath"             "sp11eff_cough"             
[115] "sp11eff_none"               "sp12bdr"                    "sp13osata"                 
[118] "trial_arm"                  "sp14bcode"                  "sp15comm"                  
[121] "sp16dater"                  "sp17date2"                  "collection_date_redcap"    
[124] "sp18labnopr"                "comments_from_lab"          "names"                     
[127] "all_3"                      "visit012"                   "visit1218"                 
[130] "visit018"                   "only2visits"                "one_visit"                 
[133] "studyno_baseline"           "ageyenrl_baseline"          "agegpenrl_baseline"        
[136] "ageyart_baseline"           "duryart_baseline"           "duryartgp_baseline"        
[139] "weightcat_baseline"         "FEV1_baseline.1"            "f25cough_baseline"         
[142] "f24mrcscore_baseline"       "f36rr_baseline"             "f37hr_baseline"            
[145] "f38sat_baseline"            "f11cotri_baseline"          "f14adm_baseline"           
[148] "f15nadm_baseline"           "f16tbtreat_baseline"        "f17notbtreat_baseline"     
[151] "f18breathe_baseline"        "f27sputum_baseline"         "attendschool_baseline"     
[154] "sc05gradesc_baseline"       "sc06misssc_baseline"        "sc08repsc_baseline"        
[157] "cd4enrl_baseline"           "vlenrl_baseline"            "datehiv_baseline"          
[160] "artdrug1stline_baseline"    "artdrug2ndline_baseline"    "ysincediag_baseline"       
[163] "ageatdiagy_baseline"        "ageatarty_baseline"         "shutm_baseline"            
[166] "artreg_baseline"            "mrcgr1_baseline"            "bmienrl_baseline"          
[169] "zwaukenrl_baseline"         "zhaukenrl_baseline"         "zbmiukenrl_baseline"       
[172] "vlenrllog_baseline"         "vlenrlsup_baseline"         "vlenrlsup400_baseline"     
[175] "vlenrlsup40_baseline"       "normalbmi_baseline"         "normalwaz_baseline"        
[178] "normalhaz_baseline"         "durartgp_baseline"          "cd4gp_baseline"            
[181] "agegpsym_baseline"          "abnrr_baseline"             "abnsat_baseline"           
[184] "abnhr_baseline"            
summary(Trial$templateDNA_16SqPCR_copies)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    169   25952   95830  355481  346819 7786891 
Trial$templateDNA_16SqPCR_copies <-log10(Trial$templateDNA_16SqPCR_copies)
summary(Trial$templateDNA_16SqPCR_copies)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.227   4.414   4.982   4.952   5.540   6.891 
#------Trial arms- How many samples were collected from site-at each timepoint------------

#use trial arms data and split into timepoints to compute data collected at each for each arm
tmpt_1 <-Trial[which(Trial$visit=="Baseline"),]#331
tmpt_12<-Trial[which(Trial$visit=="Week 48"),]#304
tmpt_18<-Trial[which(Trial$visit=="Week 72"),]#240

#---Prevalence Totals in main trial data-------------
#Subsetting data into AZM and placebo
AZM<-Trial[which(Trial$trial_arm=="AZM"),]#441
Placebo<-Trial[which(Trial$trial_arm=="Placebo"),]#434

AZM1<-tmpt_1[which(tmpt_1$trial_arm=="AZM"),]#164
AZM12<-tmpt_12[which(tmpt_12$trial_arm=="AZM"),]#154
AZM18<-tmpt_18[which(tmpt_18$trial_arm=="AZM"),]#123

AZM012<-AZM[which(AZM$visit!="Week 72"),]
AZM012p<-AZM[which(AZM$visit!="Week 72" & AZM$visit012=="yes"),]
AZM1218p<-AZM[which(AZM$visit!="Baseline" & AZM$visit1218=="yes"),]
AZM018p<-AZM[which(AZM$visit!="Week 48" & AZM$visit018=="yes"),]
AZM3p<-AZM[which(AZM$all_3=="yes"),]



Plac1<-tmpt_1[which(tmpt_1$trial_arm=="Placebo"),]#167
Plac12<-tmpt_12[which(tmpt_12$trial_arm=="Placebo"),]#150
Plac18<-tmpt_18[which(tmpt_18$trial_arm=="Placebo"),]#117

Placebo012p<-Placebo[which(Placebo$visit!="Week 72" & Placebo$visit012=="yes"),]
Placebo1218p<-Placebo[which(Placebo$visit!="Baseline" & Placebo$visit1218=="yes"),]
Placebo018p<-Placebo[which(Placebo$visit!="Week 48" & Placebo$visit018=="yes"),]
Placebo3p<-Placebo[which(Placebo$all_3=="yes"),]
summary(AZM12$templateDNA_16SqPCR_copies)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  2.406   4.077   4.646   4.620   5.207   6.378 

#AZM and Placebo at all visits

rr azmplac<-ggplot(Trial, aes(x=trial_arm, y=templateDNA_16SqPCR_copies, shape=trial_arm, fill=trial_arm)) + geom_jitter(width = 0.2)+ geom_boxplot()+ scale_fill_manual(values=c(#A087BC, #FFF468))+ facet_wrap()+coord_cartesian(ylim = c(2, 8))+ #stat_summary(fun = ,size = 0.5)+ stat_compare_means( method= .test,comparisons=list(c(, )), size=6)+ labs(x=NULL, y=\16S copies in log10)+my16stheme azmplac

#AZM at baseline and 48 weeks

rr fig1<-ggpaired(AZM012p,x=, y=_16SqPCR_copies, line.size = 0.1,line.color = #5C5B58, fill = , palette= c(#E1D0FF, #8662BD))+coord_cartesian(ylim = c(2, 8))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 48)), size=6)+ labs(x=at Baseline and 48 (n=146), y=\16S copies in log10)+my16stheme+theme(legend.position=) fig1

#AZM at 48 and 72 weeks

rr fig2<-ggpaired(AZM1218p,x=, y=_16SqPCR_copies, line.size = 0.1, line.color = #5C5B58,fill = , palette= c(#8662BD, #32224A))+coord_cartesian(ylim = c(2, 8))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(48, 72)), size=6)+ labs(x=at Week 48 and 72 (n=116), y=\16S copies in log10)+my16stheme+theme(legend.position=) fig2

#AZM at baseline and 72 weeks

rr fig3<-ggpaired(AZM018p,x=, y=_16SqPCR_copies, line.size = 0.1,line.color = #5C5B58, fill = ,palette= c(#E1D0FF, #32224A))+coord_cartesian(ylim = c(2, 8))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 72)), size=6)+ labs(x=at Week 48 and 72 (n=115), y=\16S copies in log10)+my16stheme+theme(legend.position=) fig3

#Obtaining legends #AZM

rr library (ggsci) fig1leg<-ggpaired(AZM3p,x=, y=_16SqPCR_copies, line.size = 0.1,line.color = , fill = , palette= c(#E1D0FF,#8662BD,#32224A))+coord_cartesian(ylim = c(2, 8))+ #stat_compare_means( paired=TRUE, method= ,comparisons=list(c(, 48, 72)), size=6)+ labs(x=at Baseline and 48 (n=146), y=\16S copies in log10)+my16stheme fig1leg

rr

Extract the legend. Returns a gtable

leg <- get_legend(fig1leg)

Convert to a ggplot and print

legAZM<-as_ggplot(leg)

rr library(cowplot) AZM_all_visit16Scopies<-cowplot::plot_grid(fig1,fig2, fig3,legAZM, nrow = 1, ncol = 4, rel_widths = c(2,2,2, 1) , rel_heights = c(2,2,2, 1)) AZM_all_visit16Scopies

#Placebo

rr #Placebo #Baseline and Week 48 fig4<-ggpaired(Placebo012p,x=, y=_16SqPCR_copies, line.size = 0.1, line.color = #5C5B58,fill = , palette= c(#fff9ae, #dab600))+coord_cartesian(ylim = c(2, 8))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 48)), size=6)+ labs(x=at Baseline and 48 (n=143), y=\16S copies in log10)+my16stheme+theme(legend.position=)

fig4

rr fig5<-ggpaired(Placebo1218p,x=, y=_16SqPCR_copies, line.size = 0.1, line.color = #5C5B58,fill = , palette= c(#dab600, #554904))+coord_cartesian(ylim = c(2, 8))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(48, 72)),size=6)+ labs(x=at Week 48 and 72 (n=112), y=\16S copies in log10)+my16stheme+theme(legend.position=) fig5

rr NA NA

rr fig6<-ggpaired(Placebo018p,x=, y=_16SqPCR_copies, line.size = 0.1,line.color = #5C5B58, fill = , palette= c(#fff9ae, #554904))+coord_cartesian(ylim = c(2, 8))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 72)),size=6)+ labs(x=at Baseline and 72 (n=110), y=\16S copies in log10)+my16stheme+theme(legend.position=) fig6

#Placebo legend

rr library (ggsci) #Placebo3p not working so AZM3p used but with different colors to generate the Placebo legend fig1t<-ggpaired(AZM3p,x=, y=_16SqPCR_copies, line.size = 0.1,line.color = , fill = , palette= c(#fff9ae,#dab600, #554904))+coord_cartesian(ylim = c(2, 8))+ #stat_compare_means( paired=TRUE, method= ,comparisons=list(c(, 48, 72)), size=6)+ labs(x=at all visits, y=\16S copies in log10)+my16stheme fig1t

rr

Extract the legend. Returns a gtable

leg <- get_legend(fig1t)

Convert to a ggplot and print

legPla<-as_ggplot(leg)

rr library(cowplot) Placebo_all_visit16Scopies<-cowplot::plot_grid(fig4,fig5,fig6,legPla, nrow = 1, ncol = 4, rel_widths = c(2,2,2, 1) , rel_heights = c(2,2,2, 1)) Placebo_all_visit16Scopies

rr tem16S<-cowplot::plot_grid(azmplac, AZM_all_visit16Scopies, Placebo_all_visit16Scopies, nrow = 3, ncol = 1, scale = .9, vjust=1.5, hjust= c(-3.5,-6.2,-5.0), labels = c(vs. Placebo at all visits, samples only at all visits, samples only at all visits), label_size = 20, label_fontfamily = , label_fontface = ) tem16S

rr

tem16Sb<-cowplot::plot_grid(azmplac, AZM_all_visit16Scopies, Placebo_all_visit16Scopies, nrow = 3, ncol = 1, scale = .85, vjust=1.5, hjust= c(-1.92,-1.82,-1.6), labels = c() AZM vs. Placebo at all visits, ) AZM samples only at all visits, ) Placebo samples only at all visits), label_size = 20, label_fontfamily = , label_fontface = , label_colour = blue) tem16Sb

ggsave(\16S_copies_final13thDec.pdf, tem16Sb, width = 55, height = 50, units = )

#ALPHA DIVERSITY

#AZM and Placebo at all visits

rr #Shannon #AZM and Placebo at three timepoint #Final azmplacA<-ggplot(Trial, aes(x=trial_arm, y=Shannon, shape=trial_arm, fill=trial_arm)) + geom_jitter(width = 0.2)+coord_cartesian(ylim = c(0, 4.5))+ geom_boxplot()+ scale_fill_manual(values=c(#A087BC, #FFF468))+ facet_wrap()+ #stat_summary(fun = , geom = ,width=0.5,size = 0.5, color = 3)+ stat_compare_means( method= .test,comparisons=list(c(, )),size=6)+ labs( x=NULL, y=index)+ my16stheme azmplacA

#AZM at baseline and 48 weeks

rr #Azithromycin #Baseline and Week 48 fig1A<-ggpaired(AZM012p,x=, y=, line.size = 0.1,line.color = #5C5B58, fill = , palette= c(#E1D0FF, #8662BD))+coord_cartesian(ylim = c(0, 4.5))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 48)),size=6)+ labs(x=\(n=146)\, y=\Shannon index\)+my16stheme+theme(legend.position=) fig1A

#AZM at 48 and 72 weeks

rr fig2A<-ggpaired(AZM1218p,x=, y=, line.size = 0.1,line.color = #5C5B58, fill = , palette= c(#8662BD, #32224A))+coord_cartesian(ylim = c(0, 4.5))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(48, 72)),size=6)+ labs(x=\(n=116)\, y=\Shannon index\)+my16stheme+theme(legend.position=, axis.title.y =element_blank(),axis.line = element_line(size = 0.1, colour = ) ) fig2A

#AZM at baseline and 72 weeks

rr fig3A<-ggpaired(AZM018p,x=, y=, line.size = 0.1, line.color = #5C5B58,fill = , palette= c(#E1D0FF, #32224A))+coord_cartesian(ylim = c(0, 4.5))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 72)),size=6)+ labs(x=\(n=115)\, y=\Shannon index\)+my16stheme+theme(legend.position=, axis.title.y =element_blank(), axis.line = element_line(size = 0.1, colour = )) fig3A

rr library(cowplot) AZM_all_visitShannon<-cowplot::plot_grid(fig1A,fig2A, fig3A,legAZM, nrow = 1, ncol = 4, rel_widths = c(2.3,2,2, 1) , rel_heights = c(2,2,2, 1), align = AZM_all_visitShannon

#Placebo

rr #Placebo #Baseline and Week 48

fig4A<-ggpaired(Placebo012p,x=, y=, line.size = 0.1,line.color = #5C5B58, fill = ,palette= c(#fff9ae, #dab600))+coord_cartesian(ylim = c(0, 5))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(,48)),size=6)+ labs(x=\(n=143)\, y=\Shannon index\)+my16stheme+theme(legend.position=) fig4A

rr fig5A<-ggpaired(Placebo1218p,x=, y=, line.size = 0.1,line.color = #5C5B58,fill = ,palette= c(#dab600, #554904))+coord_cartesian(ylim = c(0, 5))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(48, 72)),size=6)+ labs(x=\(n=112)\, y=\Shannon index\)+my16stheme+theme(legend.position=, axis.title.y =element_blank(), axis.line = element_line(size = 0.1, colour = )) fig5A

rr fig6A<-ggpaired(Placebo018p,x=, y=, line.size = 0.1, line.color = #5C5B58,fill = , palette= c(#fff9ae, #554904))+coord_cartesian(ylim = c(0, 5))+ stat_compare_means( paired=TRUE, method= .test,comparisons=list(c(, 72)),size=6)+ labs(x=\(n=110)\, y=\Shannon index\)+my16stheme+theme(legend.position=, axis.title.y =element_blank(), axis.line = element_line(size = 0.1, colour = )) fig6A

rr library(cowplot) Placebo_all_visitShannon<-cowplot::plot_grid(fig4A,fig5A,fig6A,legPla, nrow = 1, ncol = 4, rel_widths = c(2.3,2,2, 1) , rel_heights = c(2,2,2, 1), align = Placebo_all_visitShannon

rr temShannonA<-cowplot::plot_grid(azmplacA, AZM_all_visitShannon, Placebo_all_visitShannon, nrow = 3, ncol = 1, scale = .8, labels = c() AZM and Placebo at all visits, ) AZM arm only, ) Placebo arm only), label_size = 21, label_fontfamily = , label_fontface = , label_colour = blue,axis = align = vjust = c(2.5, 2.0,2.0), hjust = c(-1.6, -3.3, -2.7), rel_widths = c(2.5,5.5, 5.5)) temShannonA

ggsave(_final13thDec2021.pdf, temShannonA, width = 50, height = 50, units = )

---
title: "R Notebook"
output: html_notebook
Author: Regina Esinam Abotsi, Department of Molecular and Cell Biology, University of Cape Town, South Africa.
---


```{r}
library(ggplot2)
library("cowplot")
library(ggpubr)
library(phyloseq)
library (ggsci)
```

```{r}
my16stheme <- theme_bw()+ 
  theme(plot.title = element_text(family ="Helvetica", face = "bold", size = (25)), 
        panel.background = element_blank(),
        panel.border = element_rect(fill=NA,color = "black", size = 1),
        panel.spacing= unit(0.7,"cm"),
        strip.text= element_text(size=20, family ="Helvetica", face="bold"),
        legend.title = element_text(colour = "black",  face = "bold",family ="Helvetica",size = (20)), 
        legend.text = element_text(face = "bold", colour="black",family = "Helvetica",size = (18) ), 
        axis.title = element_text(family = "Helvetica", face = "bold", size = (20), colour = "black"),
        axis.text = element_text(family = "Helvetica",face = "bold", colour = "black", size = (18)))
```


```{r}
#Data import
phy <- readRDS("breathe_sputum_phyloseq_trial_13Dec2021.RDS")
Trial <-data.frame (sample_data(phy))

#----------__Subsettting data-----------------
colnames(Trial)
summary(Trial$templateDNA_16SqPCR_copies)
Trial$templateDNA_16SqPCR_copies <-log10(Trial$templateDNA_16SqPCR_copies)
summary(Trial$templateDNA_16SqPCR_copies)

#------Trial arms- How many samples were collected from site-at each timepoint------------

#use trial arms data and split into timepoints to compute data collected at each for each arm
tmpt_1 <-Trial[which(Trial$visit=="Baseline"),]#331
tmpt_12<-Trial[which(Trial$visit=="Week 48"),]#304
tmpt_18<-Trial[which(Trial$visit=="Week 72"),]#240

#---Prevalence Totals in main trial data-------------
#Subsetting data into AZM and placebo
AZM<-Trial[which(Trial$trial_arm=="AZM"),]#441
Placebo<-Trial[which(Trial$trial_arm=="Placebo"),]#434

AZM1<-tmpt_1[which(tmpt_1$trial_arm=="AZM"),]#164
AZM12<-tmpt_12[which(tmpt_12$trial_arm=="AZM"),]#154
AZM18<-tmpt_18[which(tmpt_18$trial_arm=="AZM"),]#123

AZM012<-AZM[which(AZM$visit!="Week 72"),]
AZM012p<-AZM[which(AZM$visit!="Week 72" & AZM$visit012=="yes"),]
AZM1218p<-AZM[which(AZM$visit!="Baseline" & AZM$visit1218=="yes"),]
AZM018p<-AZM[which(AZM$visit!="Week 48" & AZM$visit018=="yes"),]
AZM3p<-AZM[which(AZM$all_3=="yes"),]



Plac1<-tmpt_1[which(tmpt_1$trial_arm=="Placebo"),]#167
Plac12<-tmpt_12[which(tmpt_12$trial_arm=="Placebo"),]#150
Plac18<-tmpt_18[which(tmpt_18$trial_arm=="Placebo"),]#117

Placebo012p<-Placebo[which(Placebo$visit!="Week 72" & Placebo$visit012=="yes"),]
Placebo1218p<-Placebo[which(Placebo$visit!="Baseline" & Placebo$visit1218=="yes"),]
Placebo018p<-Placebo[which(Placebo$visit!="Week 48" & Placebo$visit018=="yes"),]
Placebo3p<-Placebo[which(Placebo$all_3=="yes"),]

```

```{r}

#Summary for Table 1
summary(AZM1$templateDNA_16SqPCR_copies)
sd(AZM1$templateDNA_16SqPCR_copies)
shapiro.test(AZM1$templateDNA_16SqPCR_copies)
summary(Plac1$templateDNA_16SqPCR_copies)
sd(Plac1$templateDNA_16SqPCR_copies)
shapiro.test(Plac1$templateDNA_16SqPCR_copies)
wilcox.test(AZM1$templateDNA_16SqPCR_copies,Plac1$templateDNA_16SqPCR_copies)
t.test(AZM1$templateDNA_16SqPCR_copies,Plac1$templateDNA_16SqPCR_copies)

summary(AZM1$Shannon)
shapiro.test(AZM1$Shannon)
summary(Plac1$Shannon)
shapiro.test(Plac1$Shannon)
wilcox.test(AZM1$Shannon,Plac1$Shannon )


summary(AZM1$Simpson)
shapiro.test(AZM1$Simpson)
summary(Plac1$Simpson)
shapiro.test(Plac1$Simpson)
wilcox.test(AZM1$Simpson,Plac1$Simpson )

shapiro.test(Trial$templateDNA_16SqPCR_copies)

#Summary for intext 16S results
summary(Trial$templateDNA_16SqPCR_copies)
summary(AZM1$templateDNA_16SqPCR_copies)
summary(Plac1$templateDNA_16SqPCR_copies)
summary(AZM12$templateDNA_16SqPCR_copies)
summary(Plac12$templateDNA_16SqPCR_copies)

summary(AZM18$templateDNA_16SqPCR_copies)
summary(Plac18$templateDNA_16SqPCR_copies)


summary(Plac1$Shannon)

summary(Plac12$Shannon)

wilcox.test(Shannon~visit, data=Placebo012p)
```



#AZM and Placebo at all visits
```{r}
azmplac<-ggplot(Trial, aes(x=trial_arm, y=templateDNA_16SqPCR_copies, shape=trial_arm, fill=trial_arm)) + 
  geom_jitter(width = 0.2)+
  geom_boxplot()+
  scale_fill_manual(values=c("#A087BC", "#FFF468"))+
  facet_wrap("visit")+coord_cartesian(ylim = c(2, 8))+
  #stat_summary(fun = "median",size = 0.5)+
  stat_compare_means( method= "wilcox.test",comparisons=list(c("AZM", "Placebo")), size=6)+
  labs(x=NULL,
       y="16S copies in log10")+my16stheme
 azmplac

```
#AZM at baseline and 48 weeks
```{r}
fig1<-ggpaired(AZM012p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1,line.color = "#5C5B58", fill = "visit", palette= c("#E1D0FF", "#8662BD"))+coord_cartesian(ylim = c(2, 8))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 48")), size=6)+ 
  labs(x="AZM at Baseline and 48 (n=146)",
       y="16S copies in log10")+my16stheme+theme(legend.position="none")
fig1
```
#AZM at 48 and 72 weeks
```{r}
fig2<-ggpaired(AZM1218p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1, line.color = "#5C5B58",fill = "visit", palette= c("#8662BD", "#32224A"))+coord_cartesian(ylim = c(2, 8))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Week 48", "Week 72")), size=6)+
  labs(x="AZM at Week 48 and 72 (n=116)",
       y="16S copies in log10")+my16stheme+theme(legend.position="none")
fig2
```

#AZM at baseline and 72 weeks
```{r}
fig3<-ggpaired(AZM018p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1,line.color = "#5C5B58", fill = "visit",palette= c("#E1D0FF", "#32224A"))+coord_cartesian(ylim = c(2, 8))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 72")), size=6)+
  labs(x="AZM at Week 48 and 72 (n=115)",
       y="16S copies in log10")+my16stheme+theme(legend.position="none")
fig3

```

#Obtaining legends
#AZM

```{r}
library (ggsci)
fig1leg<-ggpaired(AZM3p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1,line.color = "black", fill = "visit", palette= c("#E1D0FF","#8662BD","#32224A"))+coord_cartesian(ylim = c(2, 8))+
  #stat_compare_means( paired=TRUE, method= "anova",comparisons=list(c("Baseline", "Week 48", "Week 72")), size=6)+ 
  labs(x="AZM at visits",
       y="16S copies in log10")+my16stheme
fig1leg

# Extract the legend. Returns a gtable
leg <- get_legend(fig1leg)

# Convert to a ggplot and print
legAZM<-as_ggplot(leg)
```



```{r}
library(cowplot)
AZM_all_visit16Scopies<-cowplot::plot_grid(fig1,fig2, fig3,legAZM, nrow = 1, ncol = 4, rel_widths = c(2,2,2, 1) , rel_heights = c(2,2,2, 1))
AZM_all_visit16Scopies
```


#Placebo

```{r}
#Placebo
#Baseline and Week 48
fig4<-ggpaired(Placebo012p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1, line.color = "#5C5B58",fill = "visit", palette= c("#fff9ae", "#dab600"))+coord_cartesian(ylim = c(2, 8))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 48")), size=6)+
  labs(x="Placebo at Baseline and  48 (n=143)",
       y="16S copies in log10")+my16stheme+theme(legend.position="none")

fig4
```




```{r}
fig5<-ggpaired(Placebo1218p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1, line.color = "#5C5B58",fill = "visit",  palette= c("#dab600", "#554904"))+coord_cartesian(ylim = c(2, 8))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Week 48", "Week 72")),size=6)+
  labs(x="Placebo at Week 48 and 72 (n=112)",
       y="16S copies in log10")+my16stheme+theme(legend.position="none")
fig5


```


```{r}
fig6<-ggpaired(Placebo018p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1,line.color = "#5C5B58", fill = "visit", palette= c("#fff9ae", "#554904"))+coord_cartesian(ylim = c(2, 8))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 72")),size=6)+
  labs(x="Placebo at Baseline and 72 (n=110)",
       y="16S copies in log10")+my16stheme+theme(legend.position="none")
fig6

```

#Placebo legend
```{r}
library (ggsci)
#Placebo3p not working so AZM3p used but with different colors to generate the Placebo legend
fig1t<-ggpaired(AZM3p,x="visit", y="templateDNA_16SqPCR_copies", line.size = 0.1,line.color = "black", fill = "visit", palette= c("#fff9ae","#dab600", "#554904"))+coord_cartesian(ylim = c(2, 8))+
  #stat_compare_means( paired=TRUE, method= "anova",comparisons=list(c("Baseline", "Week 48", "Week 72")), size=6)+ 
  labs(x="Placebo at all visits",
       y="16S copies in log10")+my16stheme
fig1t

# Extract the legend. Returns a gtable
leg <- get_legend(fig1t)

# Convert to a ggplot and print
legPla<-as_ggplot(leg)
```




```{r}
library(cowplot)
Placebo_all_visit16Scopies<-cowplot::plot_grid(fig4,fig5,fig6,legPla, nrow = 1, ncol = 4, rel_widths = c(2,2,2, 1) , rel_heights = c(2,2,2, 1))
Placebo_all_visit16Scopies

```

```{r}
tem16S<-cowplot::plot_grid(azmplac, AZM_all_visit16Scopies, Placebo_all_visit16Scopies, nrow = 3, ncol = 1, scale = .9, vjust=1.5, hjust= c(-3.5,-6.2,-5.0), labels = c("AZM vs. Placebo at all visits", "AZM samples only at all visits", "Placebo samples only at all visits"), label_size = 20, label_fontfamily = "Helvetica", label_fontface = "bold")
tem16S

tem16Sb<-cowplot::plot_grid(azmplac, AZM_all_visit16Scopies, Placebo_all_visit16Scopies, nrow = 3, ncol = 1, scale = .85, vjust=1.5, hjust= c(-1.92,-1.82,-1.6), labels = c("A) AZM vs. Placebo at all visits", "B) AZM samples only at all visits", "C) Placebo samples only at all visits"), label_size = 20, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue")
tem16Sb


ggsave("16S_copies_final13thDec.pdf", tem16Sb,  width = 55, height = 50, units = "cm")
```




#ALPHA DIVERSITY

#AZM and Placebo at all visits
```{r}
#Shannon
#AZM and Placebo at three timepoint
#Final
azmplacA<-ggplot(Trial, aes(x=trial_arm, y=Shannon, shape=trial_arm, fill=trial_arm)) + 
  geom_jitter(width = 0.2)+coord_cartesian(ylim = c(0, 4.5))+
  geom_boxplot()+
  scale_fill_manual(values=c("#A087BC", "#FFF468"))+
  facet_wrap("visit")+
  #stat_summary(fun = "median", geom = "crossbar",width=0.5,size = 0.5, color = "red3")+
  stat_compare_means( method= "wilcox.test",comparisons=list(c("AZM", "Placebo")),size=6)+
  labs( x=NULL,
       y="Shannon index")+
  my16stheme
azmplacA

```





#AZM at baseline and 48 weeks

```{r}
#Azithromycin
#Baseline and Week 48
fig1A<-ggpaired(AZM012p,x="visit", y="Shannon", line.size = 0.1,line.color = "#5C5B58", fill = "visit", palette= c("#E1D0FF", "#8662BD"))+coord_cartesian(ylim = c(0, 4.5))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 48")),size=6)+
  labs(x="(n=146)",
       y="Shannon index")+my16stheme+theme(legend.position="none")
fig1A
```

#AZM at 48 and 72 weeks


```{r}
fig2A<-ggpaired(AZM1218p,x="visit", y="Shannon", line.size = 0.1,line.color = "#5C5B58", fill = "visit", palette= c("#8662BD", "#32224A"))+coord_cartesian(ylim = c(0, 4.5))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Week 48", "Week 72")),size=6)+
  labs(x="(n=116)",
       y="Shannon index")+my16stheme+theme(legend.position="none", axis.title.y =element_blank(),axis.line = element_line(size = 0.1, colour = "black") )
fig2A
```


#AZM at baseline and 72 weeks



```{r}
fig3A<-ggpaired(AZM018p,x="visit", y="Shannon", line.size = 0.1, line.color = "#5C5B58",fill = "visit", palette= c("#E1D0FF", "#32224A"))+coord_cartesian(ylim = c(0, 4.5))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 72")),size=6)+
  labs(x="(n=115)",
       y="Shannon index")+my16stheme+theme(legend.position="none", axis.title.y =element_blank(),  axis.line = element_line(size = 0.1, colour = "black"))
fig3A
```

```{r}
library(cowplot)
AZM_all_visitShannon<-cowplot::plot_grid(fig1A,fig2A, fig3A,legAZM, nrow = 1, ncol = 4, rel_widths = c(2.3,2,2, 1) , rel_heights = c(2,2,2, 1), align = "h")
AZM_all_visitShannon
```



#Placebo


```{r}
#Placebo
#Baseline and Week 48

fig4A<-ggpaired(Placebo012p,x="visit", y="Shannon", line.size = 0.1,line.color = "#5C5B58", fill = "visit",palette= c("#fff9ae", "#dab600"))+coord_cartesian(ylim = c(0, 5))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline","Week 48")),size=6)+
  labs(x="(n=143)",
       y="Shannon index")+my16stheme+theme(legend.position="none")
fig4A
```


```{r}
fig5A<-ggpaired(Placebo1218p,x="visit", y="Shannon", line.size = 0.1,line.color = "#5C5B58",fill = "visit",palette= c("#dab600", "#554904"))+coord_cartesian(ylim = c(0, 5))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Week 48", "Week 72")),size=6)+
  labs(x="(n=112)",
       y="Shannon index")+my16stheme+theme(legend.position="none", axis.title.y =element_blank(),  axis.line = element_line(size = 0.1, colour = "black"))
fig5A
```



```{r}
fig6A<-ggpaired(Placebo018p,x="visit", y="Shannon", line.size = 0.1, line.color = "#5C5B58",fill = "visit", palette= c("#fff9ae", "#554904"))+coord_cartesian(ylim = c(0, 5))+
  stat_compare_means( paired=TRUE, method= "wilcox.test",comparisons=list(c("Baseline", "Week 72")),size=6)+
  labs(x="(n=110)",
       y="Shannon index")+my16stheme+theme(legend.position="none", axis.title.y =element_blank(),  axis.line = element_line(size = 0.1, colour = "black"))
fig6A
```



```{r}
library(cowplot)
Placebo_all_visitShannon<-cowplot::plot_grid(fig4A,fig5A,fig6A,legPla, nrow = 1, ncol = 4, rel_widths = c(2.3,2,2, 1) , rel_heights = c(2,2,2, 1), align = "h")
Placebo_all_visitShannon
```




```{r}
temShannonA<-cowplot::plot_grid(azmplacA, AZM_all_visitShannon, Placebo_all_visitShannon, nrow = 3, ncol = 1, scale = .8, labels = c("A) AZM and Placebo at all visits", "B) AZM arm only", "C) Placebo arm only"), label_size = 21, label_fontfamily = "Helvetica", label_fontface = "bold", label_colour = "dark blue",axis = "r",align = "h", vjust = c(2.5, 2.0,2.0), hjust = c(-1.6, -3.3, -2.7),  rel_widths = c(2.5,5.5, 5.5))
temShannonA


ggsave("Shannon_final13thDec2021.pdf", temShannonA,  width = 50, height = 50, units = "cm")
```


